Insights into Antidepressant Prescribing Using Open Health Data

Cleland, Brian; Wallace, Jonathan; Bond, Raymond; Black, Michaela; Mulvenna, Maurice; Rankin, Debbie; Tanney, Austin

doi:10.1016/j.bdr.2018.02.002

Cited by 22 publications

(16 citation statements)

References 17 publications

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“…A systematic review has shown that data linkage is used in the field of perinatal health for both health surveillance and research purposes in European countries [8]. Several other studies have shown that linkage is used to explore various dynamics of population health such as social care, psychotic disorders, multi-morbidity, diabetes, obesity, mental health, cardiovascular, antibiotic use and Alzheimer using data linkage with different types of administrative data sources (both related to health and non-health) [7,[18][19][20][21][22][23][24][25][26][27][28][29][30]. For the surveillance of cancer, data linkage not only provides the opportunity to improve the population-based screening [31] but it also helps to detect different types of cancer recurrence [32] and to evaluate the socio-economic status of patients with cancer (e.g., return to work) [33].…”

Section: Discussionmentioning

confidence: 99%

Innovative use of data sources: a cross-sectional study of data linkage and artificial intelligence practices across European countries

et al. 2020

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Background: The availability of data generated from different sources is increasing with the possibility to link these data sources with each other. However, linked administrative data can be complex to use and may require advanced expertise and skills in statistical analysis. The main objectives of this study were to describe the current use of data linkage at the individual level and artificial intelligence (AI) in routine public health activities, to identify the related estimated health indicators (i.e., outcome and intervention indicators) and health determinants of noncommunicable diseases and the obstacles to linking different data sources. Method: We performed a survey across European countries to explore the current practices applied by national institutes of public health, health information and statistics for innovative use of data sources (i.e., the use of data linkage and/or AI). Results: The use of data linkage and AI at national institutes of public health, health information and statistics in Europe varies. The majority of European countries use data linkage in routine by applying a deterministic method or a combination of two types of linkages (i.e., deterministic & probabilistic) for public health surveillance and research purposes. The use of AI to estimate health indicators is not frequent at national institutes of public health, health information and statistics. Using linked data, 46 health outcome indicators, 34 health determinants and 23 health intervention indicators were estimated in routine. The complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as obstacles to routine data linkage for public health surveillance and research.

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Section: Discussionmentioning

confidence: 99%

Innovative use of data sources: a cross-sectional study of data linkage and artificial intelligence practices across European countries

et al. 2020

View full text Add to dashboard Cite

show abstract

“…A systematic review has shown that data linkage is used in the eld of perinatal health for both health surveillance and research purposes in European countries [8]. Several other studies have shown that linkage is used to explore various dynamics of population health such as social care, psychotic disorders, multimorbidity, diabetes, obesity, mental health, cardiovascular, antibiotic use and Alzheimer using data linkage with different types of administrative data sources (both related to health and non-health) [7,[18][19][20][21][22][23][24][25][26][27][28][29][30]. For the surveillance of cancer, data linkage not only provides the opportunity to improve the population-based screening [31] but it also helps to detect different types of cancer recurrence [32] and to evaluate the socioeconomic status of patients with cancer (e.g., return to work) [33].…”

Section: Discussionmentioning

confidence: 99%

Innovative use of data sources: A Cross-sectional study of Data Linkage and Artificial Intelligence Practices across European Countries

Haneef

Delnord

Vernay

et al. 2020

Preprint

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Background: The availability of data generated from different sources is increasing with the possibility to link these data sources with each other. However, linked administrative data can be complex to use and may require advanced expertise and skills in statistical analysis. The main objectives of this study were to describe the current use of data linkage at the individual level and artificial intelligence (AI) in routine public health activities, to identify the related estimated health indicators (i.e., outcome and intervention indicators) and health determinants of non-communicable diseases and the obstacles to linking different data sources. Method: We performed a survey across European countries to explore the current practices applied by national institutes of public health, health information and statistics for innovative use of data sources (i.e., the use of data linkage and/or AI). Results: The use of data linkage and AI at national institutes of public health, health information and statistics in Europe varies. The majority of European countries use data linkage in routine by applying a deterministic method or a combination of two types of linkages (i.e., deterministic & probabilistic) for public health surveillance and research purposes. The use of AI to estimate health indicators is not frequent at national institutes of public health, health information and statistics. Using linked data, 46 health outcome indicators, 34 health determinants and 23 health intervention indicators were estimated in routine. The complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as obstacles to routine data linkage for public health surveillance and research. Conclusions: Our results highlight that the majority of European countries have integrated data linkage in their routine public health activities but only a few use AI. A sustainable national health information system and a robust data governance framework allowing to link different data sources are essential to support evidence-informed health policy development. Building analytical capacity and raising awareness of the added value of data linkage in national institutes is necessary for improving the use of linked data in order to improve the quality of public health surveillance and monitoring activities.

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“…Healthcare BDA has a potential to improve the quality of care and reduce the medical cost of patients by discovering the associations from massive volume of healthcare data, thereby offering a wider perspective of clinical expertise based on medical evidences and various tests [101]. Healthcare BDA also helps the clinicians and policy makers to develop public policy and service delivery based on open health prescribed data, disease prevalence data, and economic deprivation data [102]. As per the authors in [100,101,103,104], the major areas for the applications of BDA in healthcare are as follows:…”

Section: Application Of Big Data Analytics In Healthcarementioning

confidence: 99%

A Systematic Review of Healthcare Big Data

Raja

Mukherjee

Sarkar

2020

Scientific Programming

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Over the past decade, data recorded (due to digitization) in healthcare sectors have continued to increase, intriguing the thought about big data in healthcare. There already exists plenty of information, ready for analysis. Researchers are always putting their best effort to find valuable insight from the healthcare big data for quality medical services. This article provides a systematic review study on healthcare big data based on the systematic literature review (SLR) protocol. In particular, the present study highlights some valuable research aspects on healthcare big data, evaluating 34 journal articles (between 2015 and 2019) according to the defined inclusion-exclusion criteria. More specifically, the present study focuses to determine the extent of healthcare big data analytics together with its applications and challenges in healthcare adoption. Besides, the article discusses big data produced by these healthcare systems, big data characteristics, and various issues in dealing with big data, as well as how big data analytics contributes to achieve a meaningful insight on these data set. In short, the article summarizes the existing literature based on healthcare big data, and it also helps the researchers with a foundation for future study in healthcare contexts.

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Insights into Antidepressant Prescribing Using Open Health Data

Cited by 22 publications

References 17 publications

Innovative use of data sources: a cross-sectional study of data linkage and artificial intelligence practices across European countries

Innovative use of data sources: a cross-sectional study of data linkage and artificial intelligence practices across European countries

Innovative use of data sources: A Cross-sectional study of Data Linkage and Artificial Intelligence Practices across European Countries

A Systematic Review of Healthcare Big Data

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